Can We Build an NX Bit for LLMs (www.bogdandeac.com)

The discussion around securing large language models (LLMs) from vulnerabilities like prompt injection has led to the intriguing concept of implementing an NX bit equivalent for LLMs. Prompt injection poses significant risks as LLMs struggle to differentiate between user instructions and input data, akin to outdated buffer overflow vulnerabilities in computing. Traditional defenses, including input filtering and detection systems, fall short as they are inherently probabilistic and lack absolute guarantees. Promising research on "Structured Queries" introduces a framework where special delimiter tokens help distinguish trusted instructions from untrusted data, training models to respect these boundaries. While this approach does not eliminate risks entirely, it elevates secure interactions with LLMs by improving the integrity of the instruction set versus user input. This advancement is significant for the AI/ML community as it points toward more robust defenses against prompt injection, ultimately enhancing the security and reliability of AI applications.